Overview

Dataset statistics

Number of variables9
Number of observations3144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory749.4 KiB
Average record size in memory244.1 B

Variable types

Numeric6
Categorical3

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
area_name has a high cardinality: 3144 distinct valuesHigh cardinality
fips is highly overall correlated with state and 1 other fieldsHigh correlation
urban_influence_code_2013 is highly overall correlated with unemployed_2020 and 1 other fieldsHigh correlation
unemployment_rate_2019 is highly overall correlated with unemployment_rate_2020High correlation
unemployed_2020 is highly overall correlated with urban_influence_code_2013High correlation
unemployment_rate_2020 is highly overall correlated with unemployment_rate_2019High correlation
state is highly overall correlated with fipsHigh correlation
metro_2013 is highly overall correlated with fips and 1 other fieldsHigh correlation
area_name is uniformly distributedUniform
fips has unique valuesUnique
area_name has unique valuesUnique

Reproduction

Analysis started2023-01-17 14:00:56.104978
Analysis finished2023-01-17 14:03:32.653075
Duration2 minutes and 36.55 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

fips
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30361.684
Minimum1001
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T15:03:32.697643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile5087.3
Q118174.5
median29174
Q345079.5
95-th percentile53062.7
Maximum56045
Range55044
Interquartile range (IQR)26905

Descriptive statistics

Standard deviation15180.112
Coefficient of variation (CV)0.49997595
Kurtosis-1.0975944
Mean30361.684
Median Absolute Deviation (MAD)12020
Skewness-0.080747765
Sum95457134
Variance2.3043579 × 108
MonotonicityStrictly increasing
2023-01-17T15:03:32.770237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
39087 1
 
< 0.1%
39091 1
 
< 0.1%
39093 1
 
< 0.1%
39095 1
 
< 0.1%
39097 1
 
< 0.1%
39099 1
 
< 0.1%
39101 1
 
< 0.1%
39103 1
 
< 0.1%
39105 1
 
< 0.1%
Other values (3134) 3134
99.7%
ValueCountFrequency (%)
1001 1
< 0.1%
1003 1
< 0.1%
1005 1
< 0.1%
1007 1
< 0.1%
1009 1
< 0.1%
1011 1
< 0.1%
1013 1
< 0.1%
1015 1
< 0.1%
1017 1
< 0.1%
1019 1
< 0.1%
ValueCountFrequency (%)
56045 1
< 0.1%
56043 1
< 0.1%
56041 1
< 0.1%
56039 1
< 0.1%
56037 1
< 0.1%
56035 1
< 0.1%
56033 1
< 0.1%
56031 1
< 0.1%
56029 1
< 0.1%
56027 1
< 0.1%

state
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size181.3 KiB
TX
254 
GA
 
159
VA
 
133
KY
 
120
MO
 
115
Other values (46)
2363 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6288
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL

Common Values

ValueCountFrequency (%)
TX 254
 
8.1%
GA 159
 
5.1%
VA 133
 
4.2%
KY 120
 
3.8%
MO 115
 
3.7%
KS 105
 
3.3%
IL 102
 
3.2%
NC 100
 
3.2%
IA 99
 
3.1%
TN 95
 
3.0%
Other values (41) 1862
59.2%

Length

2023-01-17T15:03:32.837350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx 254
 
8.1%
ga 159
 
5.1%
va 133
 
4.2%
ky 120
 
3.8%
mo 115
 
3.7%
ks 105
 
3.3%
il 102
 
3.2%
nc 100
 
3.2%
ia 99
 
3.1%
tn 95
 
3.0%
Other values (41) 1862
59.2%

Most occurring characters

ValueCountFrequency (%)
A 822
13.1%
N 663
 
10.5%
M 510
 
8.1%
I 501
 
8.0%
T 456
 
7.3%
O 380
 
6.0%
K 334
 
5.3%
L 300
 
4.8%
S 299
 
4.8%
C 277
 
4.4%
Other values (14) 1746
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6288
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 822
13.1%
N 663
 
10.5%
M 510
 
8.1%
I 501
 
8.0%
T 456
 
7.3%
O 380
 
6.0%
K 334
 
5.3%
L 300
 
4.8%
S 299
 
4.8%
C 277
 
4.4%
Other values (14) 1746
27.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 6288
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 822
13.1%
N 663
 
10.5%
M 510
 
8.1%
I 501
 
8.0%
T 456
 
7.3%
O 380
 
6.0%
K 334
 
5.3%
L 300
 
4.8%
S 299
 
4.8%
C 277
 
4.4%
Other values (14) 1746
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 822
13.1%
N 663
 
10.5%
M 510
 
8.1%
I 501
 
8.0%
T 456
 
7.3%
O 380
 
6.0%
K 334
 
5.3%
L 300
 
4.8%
S 299
 
4.8%
C 277
 
4.4%
Other values (14) 1746
27.8%

area_name
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct3144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size230.6 KiB
Autauga County, AL
 
1
Lawrence County, OH
 
1
Logan County, OH
 
1
Lorain County, OH
 
1
Lucas County, OH
 
1
Other values (3139)
3139 

Length

Max length47
Median length35
Mean length18.050891
Min length14

Characters and Unicode

Total characters56752
Distinct characters58
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3144 ?
Unique (%)100.0%

Sample

1st rowAutauga County, AL
2nd rowBaldwin County, AL
3rd rowBarbour County, AL
4th rowBibb County, AL
5th rowBlount County, AL

Common Values

ValueCountFrequency (%)
Autauga County, AL 1
 
< 0.1%
Lawrence County, OH 1
 
< 0.1%
Logan County, OH 1
 
< 0.1%
Lorain County, OH 1
 
< 0.1%
Lucas County, OH 1
 
< 0.1%
Madison County, OH 1
 
< 0.1%
Mahoning County, OH 1
 
< 0.1%
Marion County, OH 1
 
< 0.1%
Medina County, OH 1
 
< 0.1%
Meigs County, OH 1
 
< 0.1%
Other values (3134) 3134
99.7%

Length

2023-01-17T15:03:32.904169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county 3000
31.1%
tx 254
 
2.6%
ga 159
 
1.6%
va 133
 
1.4%
ky 120
 
1.2%
mo 115
 
1.2%
ks 105
 
1.1%
il 102
 
1.1%
nc 100
 
1.0%
ia 99
 
1.0%
Other values (1917) 5474
56.7%

Most occurring characters

ValueCountFrequency (%)
6517
 
11.5%
n 4881
 
8.6%
o 4747
 
8.4%
t 4054
 
7.1%
C 3696
 
6.5%
u 3589
 
6.3%
y 3399
 
6.0%
, 3143
 
5.5%
a 2254
 
4.0%
e 2178
 
3.8%
Other values (48) 18294
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34230
60.3%
Uppercase Letter 12810
 
22.6%
Space Separator 6517
 
11.5%
Other Punctuation 3185
 
5.6%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4881
14.3%
o 4747
13.9%
t 4054
11.8%
u 3589
10.5%
y 3399
9.9%
a 2254
6.6%
e 2178
6.4%
r 1616
 
4.7%
i 1275
 
3.7%
l 1270
 
3.7%
Other values (16) 4967
14.5%
Uppercase Letter
ValueCountFrequency (%)
C 3696
28.9%
A 960
 
7.5%
M 821
 
6.4%
N 736
 
5.7%
S 583
 
4.6%
T 555
 
4.3%
I 538
 
4.2%
L 524
 
4.1%
O 462
 
3.6%
K 422
 
3.3%
Other values (16) 3513
27.4%
Other Punctuation
ValueCountFrequency (%)
, 3143
98.7%
. 27
 
0.8%
/ 11
 
0.3%
' 4
 
0.1%
Space Separator
ValueCountFrequency (%)
6517
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47040
82.9%
Common 9712
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4881
 
10.4%
o 4747
 
10.1%
t 4054
 
8.6%
C 3696
 
7.9%
u 3589
 
7.6%
y 3399
 
7.2%
a 2254
 
4.8%
e 2178
 
4.6%
r 1616
 
3.4%
i 1275
 
2.7%
Other values (42) 15351
32.6%
Common
ValueCountFrequency (%)
6517
67.1%
, 3143
32.4%
. 27
 
0.3%
/ 11
 
0.1%
- 10
 
0.1%
' 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6517
 
11.5%
n 4881
 
8.6%
o 4747
 
8.4%
t 4054
 
7.1%
C 3696
 
6.5%
u 3589
 
6.3%
y 3399
 
6.0%
, 3143
 
5.5%
a 2254
 
4.0%
e 2178
 
3.8%
Other values (48) 18294
32.2%

urban_influence_code_2013
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2732517
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T15:03:32.966875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.500797
Coefficient of variation (CV)0.66387822
Kurtosis-1.1204878
Mean5.2732517
Median Absolute Deviation (MAD)3
Skewness0.39954967
Sum16579.103
Variance12.25558
MonotonicityNot monotonic
2023-01-17T15:03:33.024943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 733
23.3%
1 432
13.7%
6 344
10.9%
8 269
 
8.6%
5 242
 
7.7%
10 189
 
6.0%
9 184
 
5.9%
12 182
 
5.8%
7 162
 
5.2%
4 149
 
4.7%
Other values (3) 258
 
8.2%
ValueCountFrequency (%)
1 432
13.7%
2 733
23.3%
3 130
 
4.1%
4 149
 
4.7%
5 242
 
7.7%
6 344
10.9%
7 162
 
5.2%
8 269
 
8.6%
9 184
 
5.9%
10 189
 
6.0%
ValueCountFrequency (%)
12 182
5.8%
11 125
 
4.0%
10.03448276 3
 
0.1%
10 189
6.0%
9 184
5.9%
8 269
8.6%
7 162
5.2%
6 344
10.9%
5 242
7.7%
4 149
4.7%

metro_2013
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size184.3 KiB
0.0
1979 
1.0
1165 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9432
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1979
62.9%
1.0 1165
37.1%

Length

2023-01-17T15:03:33.093037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-17T15:03:33.153447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1979
62.9%
1.0 1165
37.1%

Most occurring characters

ValueCountFrequency (%)
0 5123
54.3%
. 3144
33.3%
1 1165
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6288
66.7%
Other Punctuation 3144
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5123
81.5%
1 1165
 
18.5%
Other Punctuation
ValueCountFrequency (%)
. 3144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5123
54.3%
. 3144
33.3%
1 1165
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5123
54.3%
. 3144
33.3%
1 1165
 
12.4%

unemployment_rate_2019
Real number (ℝ)

Distinct103
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9515454
Minimum0.8
Maximum20.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T15:03:33.215116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.3
Q13
median3.7
Q34.6
95-th percentile6.4
Maximum20.9
Range20.1
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.4739511
Coefficient of variation (CV)0.37300624
Kurtosis14.556121
Mean3.9515454
Median Absolute Deviation (MAD)0.8
Skewness2.4885905
Sum12423.659
Variance2.1725318
MonotonicityNot monotonic
2023-01-17T15:03:33.292182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2 138
 
4.4%
3.1 125
 
4.0%
3 116
 
3.7%
2.9 115
 
3.7%
3.7 113
 
3.6%
4 110
 
3.5%
3.5 109
 
3.5%
3.4 109
 
3.5%
3.3 108
 
3.4%
2.7 106
 
3.4%
Other values (93) 1995
63.5%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
1.4 2
 
0.1%
1.5 3
 
0.1%
1.6 8
 
0.3%
1.7 9
 
0.3%
1.8 7
 
0.2%
1.9 15
 
0.5%
2 22
0.7%
2.1 19
0.6%
2.2 40
1.3%
ValueCountFrequency (%)
20.9 1
< 0.1%
17.6 1
< 0.1%
16.8 1
< 0.1%
16 1
< 0.1%
13 2
0.1%
12.6 2
0.1%
12.5 1
< 0.1%
12.4 1
< 0.1%
11.5 1
< 0.1%
11.2 1
< 0.1%

unemployed_2020
Real number (ℝ)

Distinct2014
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4114.7088
Minimum4
Maximum629811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T15:03:33.375782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile54
Q1295
median772.5
Q32192
95-th percentile17317.6
Maximum629811
Range629807
Interquartile range (IQR)1897

Descriptive statistics

Standard deviation17035.506
Coefficient of variation (CV)4.1401486
Kurtosis620.60735
Mean4114.7088
Median Absolute Deviation (MAD)604
Skewness19.837987
Sum12936644
Variance2.9020845 × 108
MonotonicityNot monotonic
2023-01-17T15:03:33.451318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
256 11
 
0.3%
27 8
 
0.3%
12 8
 
0.3%
245 7
 
0.2%
45 7
 
0.2%
29 6
 
0.2%
449 6
 
0.2%
328 6
 
0.2%
378 6
 
0.2%
159 6
 
0.2%
Other values (2004) 3073
97.7%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
0.1%
10 3
 
0.1%
11 1
 
< 0.1%
12 8
0.3%
14 2
 
0.1%
15 2
 
0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
629811 1
< 0.1%
281454 1
< 0.1%
204310 1
< 0.1%
172361 1
< 0.1%
165513 1
< 0.1%
144278 1
< 0.1%
141814 1
< 0.1%
137723 1
< 0.1%
136563 1
< 0.1%
110086 1
< 0.1%

unemployment_rate_2020
Real number (ℝ)

Distinct142
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7461975
Minimum1.7
Maximum22.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T15:03:33.530678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile3.4
Q15.2
median6.5
Q38
95-th percentile10.6
Maximum22.5
Range20.8
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.2845938
Coefficient of variation (CV)0.33864911
Kurtosis2.8423251
Mean6.7461975
Median Absolute Deviation (MAD)1.4
Skewness0.94664081
Sum21210.045
Variance5.2193688
MonotonicityNot monotonic
2023-01-17T15:03:33.605820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.4 75
 
2.4%
5.9 65
 
2.1%
6.5 64
 
2.0%
6.2 63
 
2.0%
6.6 62
 
2.0%
6.3 61
 
1.9%
7.1 61
 
1.9%
6.4 61
 
1.9%
5.8 60
 
1.9%
5.5 58
 
1.8%
Other values (132) 2514
80.0%
ValueCountFrequency (%)
1.7 3
 
0.1%
1.8 4
 
0.1%
2 5
 
0.2%
2.1 3
 
0.1%
2.2 5
 
0.2%
2.3 6
 
0.2%
2.4 6
 
0.2%
2.5 10
0.3%
2.6 4
 
0.1%
2.7 19
0.6%
ValueCountFrequency (%)
22.5 1
< 0.1%
21.5 1
< 0.1%
19.4 1
< 0.1%
18.4 1
< 0.1%
17.8 2
0.1%
17.3 1
< 0.1%
17.1 1
< 0.1%
16.2 2
0.1%
16.1 2
0.1%
16 2
0.1%
Distinct3140
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.455654
Minimum39.92252
Maximum234.5226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T15:03:33.683731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum39.92252
5-th percentile62.338608
Q176.419744
median86.824825
Q399.43825
95-th percentile125.47682
Maximum234.5226
Range194.60008
Interquartile range (IQR)23.018505

Descriptive statistics

Standard deviation19.838551
Coefficient of variation (CV)0.22176968
Kurtosis3.0958027
Mean89.455654
Median Absolute Deviation (MAD)11.295391
Skewness1.1162484
Sum281248.58
Variance393.56812
MonotonicityNot monotonic
2023-01-17T15:03:33.764808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.56681324 3
 
0.1%
97.88211823 2
 
0.1%
79.32849121 2
 
0.1%
112.4818878 1
 
< 0.1%
111.9105988 1
 
< 0.1%
81.79681396 1
 
< 0.1%
87.69248962 1
 
< 0.1%
133.789856 1
 
< 0.1%
74.5332489 1
 
< 0.1%
114.2596741 1
 
< 0.1%
Other values (3130) 3130
99.6%
ValueCountFrequency (%)
39.92251968 1
< 0.1%
44.14705276 1
< 0.1%
44.70130539 1
< 0.1%
45.0443573 1
< 0.1%
45.57546234 1
< 0.1%
45.81298828 1
< 0.1%
46.38410568 1
< 0.1%
46.91713333 1
< 0.1%
47.17213058 1
< 0.1%
47.99232101 1
< 0.1%
ValueCountFrequency (%)
234.5225983 1
< 0.1%
213.4583435 1
< 0.1%
198.5144653 1
< 0.1%
193.5701141 1
< 0.1%
187.8100281 1
< 0.1%
186.9413605 1
< 0.1%
182.9410858 1
< 0.1%
180.2631073 1
< 0.1%
168.1533813 1
< 0.1%
167.2503357 1
< 0.1%

Interactions

2023-01-17T15:02:53.812334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:00:56.429432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:01:51.400090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:06.749039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:22.280116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:38.243447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:03:32.085422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:01:17.998473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:06.404467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:21.840742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:37.875989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:53.478953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:03:32.160763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:01:24.685381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:06.469315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:21.920149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:37.947893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:53.542911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:03:32.240220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:01:31.344172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:06.541407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:22.021936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:38.025402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:53.613167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:03:32.316348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:01:37.961152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:06.609430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:22.107393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:38.099367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:53.677318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:03:32.388801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:01:44.650885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:06.672857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:22.187237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:38.163513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T15:02:53.735823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-17T15:03:33.837396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
fipsurban_influence_code_2013unemployment_rate_2019unemployed_2020unemployment_rate_2020med_hh_income_percent_of_state_total_2019statemetro_2013
fips1.000-0.021-0.0430.0120.042-0.0041.0001.000
urban_influence_code_2013-0.0211.0000.080-0.679-0.203-0.4370.2050.999
unemployment_rate_2019-0.0430.0801.0000.0950.714-0.4050.2600.139
unemployed_20200.012-0.6790.0951.0000.4790.3580.0840.115
unemployment_rate_20200.042-0.2030.7140.4791.000-0.1700.3080.185
med_hh_income_percent_of_state_total_2019-0.004-0.437-0.4050.358-0.1701.0000.1430.433
state1.0000.2050.2600.0840.3080.1431.0000.327
metro_20131.0000.9990.1390.1150.1850.4330.3271.000

Missing values

2023-01-17T15:03:32.512053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-17T15:03:32.605043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fipsstatearea_nameurban_influence_code_2013metro_2013unemployment_rate_2019unemployed_2020unemployment_rate_2020med_hh_income_percent_of_state_total_2019
001001ALAutauga County, AL2.01.02.71262.04.9112.481888
101003ALBaldwin County, AL2.01.02.85425.05.6115.645828
201005ALBarbour County, AL6.00.03.8605.07.069.482918
301007ALBibb County, AL1.01.03.1573.06.692.557610
401009ALBlount County, AL1.01.02.71008.04.1102.184624
501011ALBullock County, AL6.00.03.7265.05.561.629097
601013ALButler County, AL6.00.03.7801.08.877.155167
701015ALCalhoun County, AL2.01.03.63260.07.192.227310
801017ALChambers County, AL5.00.02.91078.06.881.155472
901019ALCherokee County, AL6.00.02.9530.04.688.818062
fipsstatearea_nameurban_influence_code_2013metro_2013unemployment_rate_2019unemployed_2020unemployment_rate_2020med_hh_income_percent_of_state_total_2019
313456027WYNiobrara County, WY12.00.02.848.03.873.335648
313556029WYPark County, WY11.00.04.2831.05.489.935303
313656031WYPlatte County, WY11.00.03.6233.05.086.807655
313756033WYSheridan County, WY8.00.03.6801.04.996.792236
313856035WYSublette County, WY10.00.04.7294.07.2117.993408
313956037WYSweetwater County, WY8.00.04.01532.07.4121.899567
314056039WYTeton County, WY8.00.02.8912.06.0149.408936
314156041WYUinta County, WY8.00.04.0582.06.3106.959732
314256043WYWashakie County, WY11.00.04.1211.05.383.326279
314356045WYWeston County, WY9.00.03.0148.03.989.808319